DocumentCode :
1066547
Title :
Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation
Author :
Paenke, Ingo ; Branke, Jurgen ; Jin, Yaochu
Author_Institution :
Inst. AIFB, Univ. of Karlsruhe
Volume :
10
Issue :
4
fYear :
2006
Firstpage :
405
Lastpage :
420
Abstract :
For many real-world optimization problems, the robustness of a solution is of great importance in addition to the solution´s quality. By robustness, we mean that small deviations from the original design, e.g., due to manufacturing tolerances, should be tolerated without a severe loss of quality. One way to achieve that goal is to evaluate each solution under a number of different scenarios and use the average solution quality as fitness. However, this approach is often impractical, because the cost for evaluating each individual several times is unacceptable. In this paper, we present a new and efficient approach to estimating a solution´s expected quality and variance. We propose to construct local approximate models of the fitness function and then use these approximate models to estimate expected fitness and variance. Based on a variety of test functions, we demonstrate empirically that our approach significantly outperforms the implicit averaging approach, as well as the explicit averaging approaches using existing estimation techniques reported in the literature
Keywords :
approximation theory; evolutionary computation; approximate models; evolutionary algorithms; fitness approximation; Circuit synthesis; Costs; Electric breakdown; Evolutionary computation; Job shop scheduling; Manufacturing processes; Robustness; Testing; Turbines; Uncertainty; Evolutionary optimization; fitness approximation; robustness; uncertainty;
fLanguage :
English
Journal_Title :
Evolutionary Computation, IEEE Transactions on
Publisher :
ieee
ISSN :
1089-778X
Type :
jour
DOI :
10.1109/TEVC.2005.859465
Filename :
1665030
Link To Document :
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